Various methods have been known as three-dimensional model search methods.
D2: D2 is a feature vector having the highest search accuracy in the study conducted by Osada et al. (Non-Patent Document 1). Random point groups are generated on planes of a three-dimensional model, and a histogram showing the frequency of the Euclidean distance between arbitrary two points out of randomly generated points is used as the feature vector. The distance between the feature vectors is a Manhattan distance calculated by using the histogram as a one-dimensional vector.
Spherical Harmonics Descriptor (SHD): SHD is a method proposed by Kazhdan et al. (Non-Patent Document 2). A spherical harmonics transform is performed on a voxeled three-dimensional model, and a low-frequency part of the resultant power spectrum is used as a feature vector. The distance between the feature vectors is a Euclidean distance calculated by using the obtained power spectrum as a one-dimensional vector.
Light Field Descriptor (LFD): LFD is a method proposed by Chen et al. (see Non-Patent Document 3). The vertices of a regular dodecahedron are used as viewpoints, and silhouette images of the three-dimensional model are produced from a multiplicity of viewpoints by rotating the regular dodecahedron. A Zernike moment and a Fourier spectrum of the generated silhouette images are calculated as a feature vector. The distance between feature vectors is the smallest L1 distance in combination of each vertex of the dodecahedron.
Hybrid Descriptor (DSR 472): DSR 472 is a feature vector having the highest search accuracy in the study conducted by Vranic (see Non-Patent Document 4). This feature vector was invented by Vranic, which is a combination of a Depth Buffer feature vector, a silhouette feature vector, and a Ray feature vector that is obtained by orienting a vector from the center of gravity into any direction. The distance between feature values is a Manhattan distance calculated by using the composite feature value as a one-dimensional vector.    [Non-Patent Document 1] R. Osada, T. Funkhouser, B. Chazelle, D. Dobkin, Shape Distributions, ACM, TOG, 21 (4), pp. 807-832, 2002.    [Non-Patent Document 2] M. Kazhdan, T. Funkhouser, S. Rusinkiewicz, Rotation Invariant Spherical Harmonic Representation of 3D shape Descriptors, Proc. Eurographics, ACM SIGGRAPH Symp. On Geometry Processing, pp. 156-164, 2003.    [Non-Patent Document 3] D.-Y. Chen, X.-P. Tian, Y.-T. Shen, M. Ouhyoung, On Visual Similarity Based 3D Model Retrieval, Computer Graphics Forum, 22 (3), pp. 223-232, 2003.    [Non-Patent Document 4] D. Vranic, 3D Model Retrieval, Ph. D. thesis, University of Leipzig, 2004.